Egocentric Gaze Estimation via Neck-Mounted Camera
Haoyu Huang, Yoichi Sato

TL;DR
This paper explores gaze estimation from a neck-mounted camera, introducing a new dataset, evaluating a transformer-based model, and proposing extensions to improve accuracy in this novel viewpoint.
Contribution
It presents the first dataset for neck-mounted gaze estimation, evaluates a transformer-based model, and introduces auxiliary tasks and co-learning approaches.
Findings
Gaze out-of-bound classification improves performance.
Co-learning approach does not significantly enhance results.
Neck-mounted gaze estimation is feasible with specialized models.
Abstract
This paper introduces neck-mounted view gaze estimation, a new task that estimates user gaze from the neck-mounted camera perspective. Prior work on egocentric gaze estimation, which predicts device wearer's gaze location within the camera's field of view, mainly focuses on head-mounted cameras while alternative viewpoints remain underexplored. To bridge this gap, we collect the first dataset for this task, consisting of approximately 4 hours of video collected from 8 participants during everyday activities. We evaluate a transformer-based gaze estimation model, GLC, on the new dataset and propose two extensions: an auxiliary gaze out-of-bound classification task and a multi-view co-learning approach that jointly trains head-view and neck-view models using a geometry-aware auxiliary loss. Experimental results show that incorporating gaze out-of-bound classification improves performance…
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Taxonomy
TopicsGaze Tracking and Assistive Technology · Visual Attention and Saliency Detection · Vestibular and auditory disorders
